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  1. ChinaTalkSelected

    China's Ministry of Commerce plans to include AI models in the export control catalog

    China's Ministry of Commerce and Ministry of Science and Technology issued Announcement No. 48 of 2026 on October 16, proposing to add 40 items to the Catalogue of Technologies Prohibited or Restricted from Export in China, of which 14 are prohibited from export and 26 are restricted from export, and to implement pre-export registration for all newly added technologies effective immediately. The draft sets three thresholds for frontier models: ≥10^26 FLOP, ≥2T parameters, or reaching a globally leading level, and also covers AI chip design, memory chips, ten-thousand-card-level clusters, agents, AIGC detection tools, training data, and cyber offense and defense capabilities; scenarios such as open source, self-built overseas data centers, and overseas tape-out may use the report-and-export channel. The public comment period runs until November 15.

    China's Ministry of Commerce and Ministry of Science and Technology issued Announcement No. 48 of 2026 on October 16, proposing to add 40 items to the Catalogue of Technologies Prohibited or Restricted from Export in China, of which 14 are prohibited from export and 26 are restricted from export, and to implement pre-export registration for all newly added technologies effective immediately. The draft sets three thresholds for frontier models: ≥10^26 FLOP, ≥2T parameters, or reaching a globally

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    #analysis#English text is being prepared.#English text is being prepared.#Open-source ecosystem
  7. Analytics India Magazine

    Vultr to deploy a 20MW AMD MI355X GPU cluster in Chennai

    Cloud infrastructure company Vultr announced it will deploy a 20MW AMD MI355X GPU cluster in Chennai, India, which will be India's largest AMD Instinct GPU deployment and one of the largest in the world. The MI355X was released last June and features 288 GB of HBM3E memory, higher than the 180 GB of Nvidia B200. Vultr operates 34 cloud data center regions in 17 countries worldwide and plans to further expand next year, introducing next-generation MI455X GPUs and the Helios rack-scale AI architecture.

    Cloud infrastructure company Vultr announced it will deploy a 20MW AMD MI355X GPU cluster in Chennai, India, which will be India's largest AMD Instinct GPU deployment and one of the largest in the world. The MI355X was released last June and features 288 GB of HBM3E memory, higher than the 180 GB of Nvidia B200. Vultr operates 34 cloud data center regions in 17 countries worldwide and plans to further expand next year, introducing next-generation MI455X GPUs and the Helios rack-scale AI architec

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    #hot-signal#Industry news#English text is being prepared.#English text is being prepared.
  11. Huxiu

    Developers use AI reverse engineering to run iOS 16 on Android phones such as Redmi

    Developer dipa-studio successfully booted and ran iOS 16.7.16 on a Redmi Note 10 Pro, able to enter the home screen, operate Settings and Control Center, and subsequently similar demonstrations appeared on devices such as the Redmi Note 12, Nothing Phone, and OnePlus 12. The porting process extensively used Claude and Codex to write driver adaptation code, and a reverse-engineering tool REA that automatically analyzes hardware interfaces also emerged. In the current version, cellular network, Bluetooth, camera, and speaker are all unavailable; GPU hardware acceleration has not been made to work, causing lag; booting takes 4 minutes; code signing verification and sandbox restrictions have also been relaxed; it is only suitable for early-adopter demonstrations.

    Developer dipa-studio successfully booted and ran iOS 16.7.16 on a Redmi Note 10 Pro, able to enter the home screen, operate Settings and Control Center, and subsequently similar demonstrations appeared on devices such as the Redmi Note 12, Nothing Phone, and OnePlus 12. The porting process extensively used Claude and Codex to write driver adaptation code, and a reverse-engineering tool REA that automatically analyzes hardware interfaces also emerged. In the current version, cellular network, Bl

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    #hot-signal#English text is being prepared.#Open-source ecosystem#Agents
  13. Latent SpaceSelected

    DeepSeek open-sources the DeepInfer inference framework, processing 155 trillion tokens per day in a single cluster

    DeepSeek has open-sourced DeepInfer, its self-developed inference framework that supports DeepSeek-V4.1-Flash, and published production data: a single cluster processes 155 trillion tokens per day, 96% input cache hit rate, 400ms first-token latency, and 230 tokens/s generation speed. The framework is customized based on vLLM and supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, with target platforms NVIDIA SM100 and Huawei Ascend 950. DeepSeek also states that components such as KV cache management and load balancing are not yet fully open-sourced, while the vLLM team says that under a matching GB200 configuration, decoding throughput is within 6% of the results published by DeepInfer.

    DeepSeek has open-sourced DeepInfer, its self-developed inference framework that supports DeepSeek-V4.1-Flash, and published production data: a single cluster processes 155 trillion tokens per day, 96% input cache hit rate, 400ms first-token latency, and 230 tokens/s generation speed. The framework is customized based on vLLM and supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, with target platforms NVIDIA SM100 and Huawei Ascend 950. DeepSeek also

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    #newsletter#English text is being prepared.#Reasoning#English text is being prepared.
  14. Latent Space

    DeepSeek open-sources the DeepInfer inference framework, processing 155 trillion tokens per day on a single cluster

    DeepSeek has open-sourced DeepInfer, the production inference framework supporting V4.1 Flash, and published production data including 155 trillion tokens processed per day on a single cluster, a 96% input cache hit rate, an average 400ms time to first token, and 230 tokens/s generation. The framework is customized based on vLLM and uses the Apache 2.0 license, supports distributed inference, speculative decoding, SWA Bounded Replay and structured output, and targets NVIDIA SM100 and Huawei Ascend 950. DeepSeek says components such as KV cache management and load balancing are not fully open-sourced because they are coupled with internal infrastructure, while the vLLM team reports that under matched GB200 configurations, decoding throughput differs from DeepInfer's published results by less than 6%.

    DeepSeek has open-sourced DeepInfer, the production inference framework supporting V4.1 Flash, and published production data including 155 trillion tokens processed per day on a single cluster, a 96% input cache hit rate, an average 400ms time to first token, and 230 tokens/s generation. The framework is customized based on vLLM and uses the Apache 2.0 license, supports distributed inference, speculative decoding, SWA Bounded Replay and structured output, and targets NVIDIA SM100 and Huawei Asce

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    #newsletter#English text is being prepared.#Reasoning#English text is being prepared.
  18. 36Kr · AI

    Mass General Brigham team publishes FAHR-Face study in The Lancet Digital Health, using facial photos to predict mortality risk in cancer patients

    The U.S. Mass General Brigham health care system developed the FAHR-Face model, which identifies differences in mortality risk among cancer patients using only a single facial photograph; the study was published on October 20 in The Lancet Digital Health. Among 3,820 cancer patients who did not participate in training, 64 of the 122 people rated by the model as extremely high risk died within 90 days after the photo was taken, while only 20 of the 975 people with scores between 0 and 0.1 died, a rate of 2.1%. The study also conducted external validation using 4,892 patients at the Maastro radiotherapy center in the Netherlands and obtained similar results, but the model cannot yet be used directly in clinical practice.

    The U.S. Mass General Brigham health care system developed the FAHR-Face model, which identifies differences in mortality risk among cancer patients using only a single facial photograph; the study was published on October 20 in The Lancet Digital Health. Among 3,820 cancer patients who did not participate in training, 64 of the 122 people rated by the model as extremely high risk died within 90 days after the photo was taken, while only 20 of the 975 people with scores between 0 and 0.1 died, a

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    #hot-signal#Papers/Research#Multimodal#English text is being prepared.